# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os

import datasets
import pandas as pd


_CITATION = """\
"""

_DESCRIPTION = """\
"""

_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"

domains = {
    "Human Science": [
        "Human Science",
        "Art & Architecture",
    ],
    "Medicine": [
        "Medical Science",
        "Veterinary Science",
    ],
    "Science & engineering": [
        "Agriculture & Natural Resources",
        "Engineering & Technology",
        "Basic Science",
    ],
}


class ESPOSITOConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)


class ESPOSITO(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        ESPOSITOConfig(
            name=domain,
        )
        for domain in domains.keys()
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id":datasets.Value("int32"),
                "question": datasets.Value("string"),
                "A": datasets.Value("string"),
                "B": datasets.Value("string"),
                "C": datasets.Value("string"),
                "D": datasets.Value("string"),
                "answer": datasets.Value("string"),
                "explanation":datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        task_name = self.config.name
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "test", f"{task_name}_test.csv"
                    ),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split("val"),
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir,  "val", f"{task_name}_val.csv"
                    ),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split("dev"),
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "dev", f"{task_name}_dev.csv"
                    ),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        df = pd.read_csv(filepath,encoding="utf-8")
        for i, instance in enumerate(df.to_dict(orient="records")):
            if "answer" not in instance.keys():
                instance["answer"]=""
            if "explanation" not in instance.keys():
                instance["explanation"]=""
            yield i, instance